



'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

lowerCAmelCase:   Union[str, Any]					=		tuple[int, int]

class 				a__:







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    set[int]		,     __snake_case					:    Mapping[EdgeT, int]      ):
      a		:   set[int]	  =	vertices
      a		:   dict[EdgeT, int]	  =	{
          (min(__snake_case      ), max(__snake_case      )): weight for edge, weight in edges.items()
      }







    def      lowercase_			( self					:    str		,     __snake_case					:    EdgeT		,     __snake_case					:    int      ):
      self.vertices.add(edge[0]      )
      self.vertices.add(edge[1]      )
      a		:   int	  =	weight







    def      lowercase_			( self					:    int      ):
      a		:   Graph	  =	Graph({min(self.vertices      )}		,     {}      )
      a		:   EdgeT
      a		:   int
      a		:   EdgeT
      a		:   int

      while len(subgraph.vertices      ) < len(self.vertices      ):
        a		:   Optional[int]	  =	max(self.edges.values()      ) + 1
        for edge, weight in self.edges.items():
          if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
            if weight < min_weight:
              a		:   Optional[Any]	  =	edge
              a		:   Optional[Any]	  =	weight

        subgraph.add_edge(__snake_case		,     __snake_case      )

      return subgraph
def  lowerCamelCase__			(		_A = "p107_network.txt"							):
  a		:   str	  =	os.path.abspath(os.path.dirname(_A							)							)
  a		:   str	  =	os.path.join(_A					,     _A							)
  a		:   dict[EdgeT, int]	  =	{}
  a		:   list[str]
  a		:   int
  a		:   int

  with open(_A							) as f:
    a		:   Optional[int]	  =	f.read().strip().split('\n'							)

  a		:   str	  =	[line.split(','							) for line in data]

  for edgea in range(1					,     len(_A							)							):
    for edgea in range(_A							):
      if adjaceny_matrix[edgea][edgea] != "-":
        a		:   Tuple	  =	int(adjaceny_matrix[edgea][edgea]							)

  a		:   Graph	  =	Graph(set(range(len(_A							)							)							)					,     _A							)

  a		:   Graph	  =	graph.prims_algorithm()

  a		:   int	  =	sum(graph.edges.values()							)
  a		:   int	  =	sum(subgraph.edges.values()							)

  return initial_total - optimal_total


if __name__ == "__main__":
     print(F"{solution() = }")




'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       ["""image_processor""", """tokenizer"""]
    lowercase__					=       """BridgeTowerImageProcessor"""
    lowercase__					=       ("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__( self					:    int		,     __snake_case					:    str		,     __snake_case					:    List[str]      ):
      super().__init__(__snake_case		,     __snake_case      )







    def __call__( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __snake_case					:    bool = True		,     __snake_case					:    Union[bool, str, PaddingStrategy] = False		,     __snake_case					:    Union[bool, str, TruncationStrategy] = None		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    int = 0		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = True		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     **__snake_case					:    List[Any]		,     ):
      a		:   Optional[int]	  =	self.tokenizer(
          text=__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     stride=__snake_case		,     pad_to_multiple_of=__snake_case		,     return_token_type_ids=__snake_case		,     return_attention_mask=__snake_case		,     return_overflowing_tokens=__snake_case		,     return_special_tokens_mask=__snake_case		,     return_offsets_mapping=__snake_case		,     return_length=__snake_case		,     verbose=__snake_case		,     return_tensors=__snake_case		,     **__snake_case		,     )
      # add pixel_values + pixel_mask
      a		:   List[str]	  =	self.image_processor(
          __snake_case		,     return_tensors=__snake_case		,     do_normalize=__snake_case		,     do_center_crop=__snake_case		,     **__snake_case      )
      encoding.update(__snake_case      )

      return encoding







    def      lowercase_			( self					:    int		,     *__snake_case					:    List[str]		,     **__snake_case					:    List[str]      ):
      return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    Tuple		,     **__snake_case					:    Union[str, Any]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.tokenizer.model_input_names
      a		:   Union[str, Any]	  =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )